Abstract
Results of the application of a new edge-defining preprocessor to the recognition of handwritten numerals are presented. The decision mechanisms which were simulated are simple and are closely related to maximum likelihood decisions. Results are presented of experiments in which 1000 numeral patterns were used to set up a memory, which was then tested on a further 1000 samples. Success rates approaching 95 percent on completely unselected data have been measured. A convenient rejection procedure is described, and the error reject curves produced are shown. These are so encouraging that the scheme must be regarded as the basis of a practical machine for many real life applications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Systems, Man, and Cybernetics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
